System and method for detecting visually similar items

ABSTRACT

A computer-implemented method includes determining a set of target listings, retrieving a seed image associated with the seed listing, the seed listing is categorized within a first item category, and generating a seed item feature vector for the seed image using a convolutional neural network (CNN) trained with images of items. The method also includes identifying a plurality of feature vectors associated with the first item category, comparing the seed item feature vector to the plurality of feature vectors using a k-nearest neighbors (kNN) algorithm, and generating a set of nearest neighbor listings to the seed listing. The method further includes storing the set of nearest neighbor listings as associated with the seed listing, selecting one or more nearest neighbor listings from the set of nearest neighbors, and presenting the one or more nearest neighbor listings as a recommendation to a user of the online e-commerce system.

RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 15/430,191, filed Feb. 10, 2017, entitled “SYSTEMAND METHOD FOR DETECTING VISUALLY SIMILAR ITEMS”, which claims thebenefit of priority to U.S. Provisional Patent Application Ser. No.62/294,178, filed Feb. 11, 2016, herein incorporated by reference in itsentirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to imageprocessing and, more particularly, but not by way of limitation, tosystems and methods for detecting similar items based on image-basedanalysis.

BACKGROUND

Some online e-commerce systems (e.g., managing e-commerce sites) allowsellers to offer items for sale. To improve consumer experiences, someonline e-commerce systems provide product recommendations to buyers. Oneknown method of generating recommendations for buyers is through“collaborative filtering,” which includes generating productrecommendations based on some known interest of a target user (e.g., aproduct recently purchased by the target user) as compared to known datafrom other users (e.g., product purchase data from other users thatpurchased the same product). However, in some situations, there may notbe enough data about the product, the target user, or other users forknown collaborative filtering methods to perform sufficiently. Further,collaborative filtering performs poorly with cross-categoryrecommendations.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and cannot be considered aslimiting its scope.

FIG. 1 is a network diagram depicting an example online e-commercesystem.

FIG. 2 is a block diagram showing components provided within the imageanalysis engine, according to some embodiments.

FIG. 3 is a dataflow diagram illustrating an example process for imageanalysis as performed by the image analysis engine.

FIG. 4 continues the dataflow diagram shown in FIG. 3 .

FIG. 5 illustrates a computerized method, in accordance with an exampleembodiment, for image-based analysis of listings.

FIG. 6 is a block diagram illustrating an example software architecture,which may be used, in conjunction with various hardware architecturesherein described, to perform image-based analysis of listings (e.g., onthe online e-commerce system shown in FIG. 1 ).

FIG. 7 is a block diagram illustrating components of a machine,according to some example embodiments, configured to read instructionsfrom a machine-readable medium (e.g., a machine-readable storage medium)and perform any one or more of the methodologies discussed herein.

The headings provided herein are merely for convenience and do notnecessarily affect the scope or meaning of the terms used. Like numbersin the Figures indicate like components.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatdescribe illustrative embodiments of the disclosure. Numerous specificdetails are set forth herein in order to provide an understanding ofvarious embodiments of the present subject matter. It will be evident,however, to those skilled in the art, that embodiments of the presentsubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

An image analysis engine is described herein for providing image-basedanalysis of items offered for sale in an online e-commerce system. Insome scenarios, the online e-commerce system has sparse or non-existentpurchase data for some items, thus limiting the possibility of usingknown behavioral signal approaches for determining similar items. Forexample, a newly released movie or an uncommon (e.g., low-selling) bookmay have generated little or no prior purchases through the onlinee-commerce system and, as a result, there may not be enough purchasehistory for the item sufficient to generate a satisfactory itemrecommendation using other methods reliant on such data. In somescenarios, some items may not have adequate data provided to leverageconventional methods for generating item recommendations (e.g., nomanufacturer ID provided, or an inadequate or undescriptive title ordescription). For example, a pair of shoes may be offered for sale by aseller, but the seller may not provide a manufacturer ID that wouldotherwise indicate data about the shoes, such as manufacturer, type ofshoe, shoe features, and so forth. In some scenarios, some items may nothave enough behavioral data associated with the item to leverageconventional methods (e.g., a low volume of prior user activity, such asclicks, views, or purchases). To avoid problems with scarce purchasedata or absent manufacturer ID, the image analysis engine and methodsdescribed herein provide image-based analysis of items offered on theonline e-commerce system to generate recommendations for visuallysimilar items (e.g., based on images of the item provided by thesellers).

Finding item similarity based on image analysis presents its own set ofchallenges. The images provided for various items may depict the itemsin different poses, or in interaction with other objects, or inchallenging backgrounds, or with differing illumination, viewpoint,occlusion, and so forth. In addition, some items are truly unique,making it impossible to find other items which are identical.

In some example embodiments, the items offered for sale by the onlinee-commerce system, and analyzed by the image analysis engine, includeone or more item images (e.g., a digital photo of the image, perhapsuploaded to the online e-commerce site by the seller of the item). Theimage may be, for example, a stock image of a “model” item generated andpromoted by the manufacturer, or may be an image, taken by the seller,of the actual item offered for sale. The image analysis engine performsimage-based analysis of the item using the associated image(s). Morespecifically, in one example embodiment, the image analysis engineperforms feature extraction on the image of the “seed item” to generatea feature vector for the image using a pre-trained Convolutional NeuralNetwork (CNN). That feature vector for the seed item is then compared tofeature vectors of other items sharing a category with the seed item onthe online e-commerce system using a k-nearest neighbors (KNN) process.The k nearest neighbors to the seed item are, thus, identified assimilar items to the seed item. The online e-commerce system then storesreferences to those nearest neighbors within a record of the seed item,thereby identifying the nearest neighbors for later use in generating asimilar items recommendation to users identified with the seed item.

In some embodiments, the CNN may utilize a third-party model such as theGoogLeNet model, as promulgated by Google® Inc. In other embodiments,the image analysis engine may train a custom CNN model based on alabeled training set of images. The training set of images may belabeled with a category, or with other fine grained data specific tothat category or a particular type of goods. For example, images ofathletic shoes may be labeled with manufacturer (e.g., Nike®, Adidas®),or with a type of athletic show (e.g., cross-fit, running, aerobic,basketball), or with particular features of athletic shoes (e.g.,shockers, reflectors, Velcro laces). Such a custom model may then beused on that particular category of items.

FIG. 1 is a network diagram depicting an example online e-commercesystem 100. In the example embodiment, the online e-commerce system 100includes a networked system 102 that provides online services to onlineusers, such as a user 106 via a client device 110. The networked system102 includes an image analysis engine 150 for performing image-basedanalysis of online items offered for sale via the online e-commercesystem 100, and other associated operations, as described herein.

The networked system 102 provides network-based, server-sidefunctionality, via a network 104 (e.g., the Internet or a Wide AreaNetwork (WAN)), to the client devices 110 that may be used, for example,by sellers or buyers (not separately shown) of products and servicesoffered for sale through a publication system 142 (e.g., through anonline marketplace system provided by the publication systems 142 orpayment systems 144). FIG. 1 further illustrates, for example, one ormore of a web client 112 (e.g., a web browser), client application(s)114, and a programmatic client 116 executing on the client device 110.

Each of the client devices 110 comprises a computing device thatincludes at least a display and communication capabilities with thenetwork 104 to access the networked system 102. The client device 110includes devices such as, but not limited to, work stations, computers,general purpose computers, Internet appliances, hand-held devices,wireless devices, portable devices, wearable computers, cellular ormobile phones, portable digital assistants (PDAs), smart phones,tablets, ultrabooks, netbooks, laptops, desktops, multi-processorsystems, microprocessor-based or programmable consumer electronics, gameconsoles, set-top boxes, network PCs, mini-computers, and the like. Eachof the client devices 110 connects with the network 104 via a wired orwireless connection. For example, one or more portions of the network104 may be an ad hoc network, an intranet, an extranet, a virtualprivate network (VPN), a local area network (LAN), a wireless LAN(WLAN), a WAN, a wireless WAN (WWAN), a metropolitan area network (MAN),a portion of the Internet, a portion of the Public Switched TelephoneNetwork (PSTN), a cellular telephone network, a wireless network, a WiFinetwork, a WiMax network, another type of network, or a combination oftwo or more such networks.

Each of the client devices 110 includes one or more client applications(also referred to as “apps”) 114 such as, but not limited to, a webbrowser, a messaging application, an electronic mail (email)application, an e-commerce site application (also referred to as amarketplace application), and the like. In some embodiments, if thee-commerce site application is included in a given one of the clientdevices 110, then this application is configured to locally provide theuser interface and at least some of the functionalities of an e-commercesite, with the application configured to communicate with the networkedsystem 102, on an as-needed basis, for data or processing capabilitiesnot locally available (e.g., such as access to a database of itemsavailable for sale, to authenticate a user, or to verify a method ofpayment). Conversely, if the e-commerce site application is not includedin a given one of the client devices 110, the given one of the clientdevices 110 may use its web client 112 to access the e-commerce site (ora variant thereof) hosted on the networked system 102. Although only oneclient device 110 is shown in FIG. 1 , two or more client devices 110may be included in the online e-commerce system 100.

An Application Program Interface (API) server 120 and a web server 122are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 140. In the example embodiment, theapplication servers 140 host the image analysis engine 150 thatfacilitates providing analysis and recommendation services, as describedherein. The application servers 140 are, in turn, shown to be coupled toone or more database servers 124 that facilitate access to one or moredatabases 126.

In some embodiments, the application servers 140 host one or morepublication systems 142 and payment systems 144. The publication system142 may provide a number of e-commerce functions and services to usersthat access the networked system 102 or external sites (e.g., a thirdparty publication system 130 executing a site application 132).E-commerce functions and services may include a number of publisherfunctions and services (e.g., search, listing, content viewing, payment,etc.). For example, the publication system 142 may provide a number ofservices and functions to users for listing or submitting offers forgoods or services for sale, searching for goods and services,facilitating transactions, and reviewing and providing feedback abouttransactions and associated users. Additionally, the publication system142 may track and store data and metadata relating to listings,transactions, and user interactions. In some embodiments, thepublication system 142 may publish or otherwise provide access tocontent items stored in the application servers 140 or the databases 126accessible to the application servers 140 or the database servers 124.The payment system 144 may likewise provide a number of payment servicesand functions to users. The payment system 144 may allow users toaccumulate value (e.g., in a commercial currency, such as the U.S.dollar, or a proprietary currency, such as “points”) in accounts, andthen later to redeem the accumulated value for items (e.g., goods orservices) that are made available via the publication system 142. Whilethe publication system 142 and the payment system 144 are shown in FIG.1 to both form part of the networked system 102, it will be appreciatedthat, in alternative embodiments, the payment system 144 may form partof a payment service that is separate and distinct from the networkedsystem 102. In other embodiments, the payment system 144 may be omittedfrom the online e-commerce system 100. In some embodiments, at least aportion of the publication system 142 may be provided on the clientdevices 110.

Further, while the online e-commerce system 100 shown in FIG. 1 employsa client-server architecture, some example embodiments of the presentdisclosure are not limited to such an architecture, and may equally wellfind application in, for example, a distributed or peer-to-peerarchitecture system. The various publication and payment systems 142 and144 may also be implemented as standalone software programs, which donot necessarily have networking capabilities.

The client devices 110 access the various publication and paymentsystems 142 and 144 via the web interface supported by the web server122. Similarly, the programmatic client 116 accesses the variousservices and functions provided by the publication and payment systems142 and 144 via the programmatic interface provided by the API server120. The programmatic client 116 may, for example, be a sellerapplication (e.g., the TurboLister application developed by eBay Inc.,of San Jose, California) to enable sellers to author and manage listingson the networked system 102 in an offline manner, and to performbatch-mode communications between the programmatic client 116 and thenetworked system 102.

In the example embodiment, the image analysis engine 150 analyzes imagesof items associated with listings on the networked system 102. Theonline e-commerce system 100 may provide item or listing recommendationsto users based on user interest, such as determined through onlineviewing, watching, or purchasing of items through the online e-commercesystem 100, as well as item similarity as determined by the systems andmethods described herein.

FIG. 2 is a block diagram showing components provided within the imageanalysis engine 150 according to some embodiments. The image analysisengine 150 may be hosted on dedicated or shared server machines (notshown) that are communicatively coupled to enable communications betweenthe server machines. In some embodiments, components of the imageanalysis engine 150 may be executed on a graphics processing unit (GPU)such as, for example, one or more Titan X GPUs (such as those madecommercially available by NVIDIA corporation, a California corporation,and other manufacturers). The components themselves are communicativelycoupled (e.g., via appropriate interfaces) to each other and to variousdata sources, so as to allow information to be passed between thecomponents or so as to allow the components to share and access commondata. Furthermore, the components may access the one or more databases126 via the database servers 124 (both shown in FIG. 1 ).

The image analysis engine 150 provides a number of image analysisfeatures related to items or listings, whereby the image analysis engine150 performs image-based analysis of images associated with items toprovide similar item recommendations to users. To this end, the imageanalysis engine 150 includes a data extractor module 210, an imagedownloader module 220, a feature extraction module 230, a KNN module240, a recommendation module 250, and a modeling module 260.

In the example embodiment, the data extractor module 210 determines aset of items (e.g., listings) to prepare for image processing. Forexample, the image analysis engine 150 may identify a set of listingsrecently added to the online e-commerce system 100 (e.g., within thelast 24 hours), or other listings that have not undergone image analysisas described herein. Each of these listings may include an item ID, aseed image of the associated item offered for sale through the listing(e.g., referenced via an image uniform resource locator (URL)), and mayadditionally identify one or more categories associated with thelisting. The image downloader module 220 retrieves the seed images foreach of the identified listings (e.g., using the image URL) and preparesthe seed images for further processing. In some embodiments, the seedimages are converted to a standard size, such as 224 by 224 pixels.

The feature extraction module 230 performs feature extraction on each ofthe seed images. In an example embodiment, the feature extraction engine230 uses the Caffe library for feature extraction based on the GoogLeNetmodel to generate a feature vector for the image, and thus for thelisting itself. As such, each seed image generates an associated featurevector. All of these feature vectors are then added to feature vectorsof previously-processed listings to form a cumulative set of featurevectors. Subsequently, for each seed item being processed, the KNNmodule 240 determines the k nearest neighbors of the seed item based onthe feature vector of the seed item and the other feature vectors ofitems within the same category as the seed item. In some embodiments,the other feature vectors may be for items in other categories. In oneexample embodiment, the KNN module 240 identifies the 30 nearestneighbors of each seed item, each of which is then treated as a similaritem to the seed item. These nearest neighbors each include anassociated listing ID. For each seed item, the recommendation module 250stores the listing IDs of the nearest neighbors of that seed item (e.g.,in a database) and, in some embodiments, may cache the nearest neighborsof that seed item in an in-memory database to facilitate prompt access.During operation, when an online shopper engages with the seed item(e.g., viewing the listing, bidding on the listing, purchasing thelisting), the recommendation module 250 accesses the nearest neighborsof the seed item and provides similar item recommendations based on theset of nearest neighbors.

In some embodiments, the modeling module 260 may generate the model usedby the feature extraction module 230 for feature extraction. Themodeling module 260 may identify a labeled training set of images withina particular category, and may create a convolutional neural networkusing the labeled training set. This custom model may then be used onseed items within that particular category to provide a more refinedfeature vector.

FIG. 3 is a dataflow diagram illustrating an example process for imageanalysis as performed by the image analysis engine 150. In the exampleembodiment, the data extractor module 210 identifies a set of targetlistings 320 from a listings database 310. The set of target listings320 includes seed listings 322 that have not yet been image-analyzed bythe image analysis engine 150. In some embodiments, the seed listings322 include listings that have been added to the online e-commercesystem 100 within a pre-determined amount of time (e.g., within the lastday, or within the last week). In some embodiments, the set of targetlistings 320 may include seed listings 322 that have been previouslyprocessed, but which may be selected for re-processing (e.g., after apre-determined amount of time, or after a pre-determined number ofsubsequent listings have been processed, to “refresh” image comparisonresults). Each seed listing 322 includes at least one image or areference to an image (e.g., an image URL 324), one or more listingcategories 326, and a listing identifier (ID) unique to that seedlisting 322 within the online e-commerce system 100.

In the example embodiment, for each of the seed listings 322 in the setof target listings 320, the image downloader module 220 retrieves anassociated image (or “listing image”) 332 from an image database 330using the image URL 324. In some embodiments, the listing images 332 maybe stored in the listings database 310. The listing images 332 mayundergo pre-processing prior to further use. For example, the imageanalysis engine 150 may convert the listing images to a standard size,such as 224 by 224 pixels, or may alter aspects of the image tofacilitate normalization of varying images.

The feature extraction module 230, in the example embodiment, thenutilizes a CNN model 340 to extract features of each image (e.g., oneimage at a time). The CNN model 340 converts a particular listing image332 into a feature vector 342 (e.g., an array of n floating-pointnumbers representing n features of the image 332). In one embodiment,the CNN model 340 is the GoogLeNet model, a third-party model which wastrained on the ImageNet dataset with the objective of classifying eachimage into the correct class out of one thousand objects. Further, thefeature extraction module 230 uses the Caffe library to perform thefeature extraction with the CNN model 340. As such, each listing imageis reduced to an associated feature vector 342.

FIG. 4 continues the dataflow diagram shown in FIG. 3 . In the exampleembodiment, a feature record 410 is created for each seed listing 322.Each feature record 410 includes the listing ID 328 and the category(s)326 of the seed listing 322, as well as the feature vector 342 justgenerated for that seed listing 322. The feature records 410 are storedin a vector database 412 for future use. The vector database 412includes feature records 410 for the recently-processed seed items 322,as well as feature records for previously-processed listings. In otherwords, the vector database 412 is a cumulative repository for featurevectors of many or all listings of the online e-commerce system 100.

In the example embodiment, the KNN module 240 operates on each seed item322 to determine a set of k nearest neighbors 430 for that seed item322. More specifically, for each seed item 322, the KNN module 240retrieves a seed item feature vector 420 for that seed item 322 as wellas a set of feature vectors 422 for all feature records in the featurevector database 412 that share at least one category 326 with the seeditem 322 (also referred to herein as category item feature vectors 422).The KNN module 240 then compares the seed item feature vector 420 to theset of category item feature vectors 422 to determine the k nearestneighbors 430. In one embodiment, the KNN module 240 utilizes bruteforce nearest neighbors on a graphics processing unit (GPU). Restrictingthe nearest neighbor search to only listings within a shared categorystrategically limits the amount of processing to only types of itemsthat are most likely to actually be similar to the seed item. Forexample, in some situations, comparing images cars to an image of a shoemay occasionally generate enough similarity to cause a car to beidentified as a nearest neighbor to the shoe (e.g., because of theirgeneral shape, color, background). As such, limiting the feature vectors422 to only those feature vectors 422 in the “shoes” category may keepsome errant listings from creeping in.

In the example embodiment, the k nearest neighbors 430 are then utilizedby the online e-commerce system 100 to generate “similar item” typerecommendations. More specifically, for each seed item 322, its knearest neighbors 430, as identified by their associated listing IDs328, are stored in the listing of the seed item 322 in the listingsdatabase 310. For performance improvement, the k nearest neighbors 430for the seed items 322 may be stored in a RAM memory-based database 440,such as Redis. As such, each listing processed by the image analysisengine 150 includes a set of k nearest neighbors that may be used as“similar items” to that seed item. For example, when a buyer of theonline e-commerce system 100 accesses the seed item (e.g., via viewingthe associated listing, bidding on that listing, buying the seed itemvia that listing), the online e-commerce system 100 may infer interestin the seed item and, as such, may utilize the k nearest neighbors 430of that listing to recommend similar listings.

In some embodiments, the image analysis engine 150 may generate a custommodel to use as the CNN model 340. For example, the image analysisengine 150 may identify a set of labeled training listings (notseparately depicted) for use in training the CNN model 340. The traininglistings may be labeled based on manufacturer, or features orcharacteristics of the item, or a listing category of the listing, oranother signal that corresponds to a similarity measure between theitems. The modeling module 260 may then train the CNN model 340 usingthe labeled training set. In some embodiments, the modeling module 260may train the CNN model 340 using a convolutional neural networkleveraging aspects of deep learning. As such, the custom CNN model 340may then be better able to determine features associated with thelabels. For example, a custom CNN model 340 trained with traininglistings of shoes labeled with a manufacturer name may better be able todistinguish between Nike® shoes and Reebok® shoes. The resultingcustomized model 340 may then be deployed to specific seed listings 322.For example, when encountering a seed listing 322 for which a custom CNNmodel 340 has been developed (e.g., when the seed listing 322 iscategorized in “shoes”), the feature extraction engine 230 may use thecustom CNN model 340 associated with shoes rather than the generic CNNmodel 340 trained across disparate types of items.

FIG. 5 illustrates a computerized method 500, in accordance with anexample embodiment, for image-based analysis of listings. Thecomputerized method 500 is performed by a computing device comprising atleast one processor and a memory. In the example embodiment, atoperation 510, the computerized method 500 includes determining a set oftarget listings, each target listing of the set of target listingsincludes a category identifier, a listing identifier, and an imageassociated with the item. In some embodiments, determining a set oftarget listings further includes determining target listings added tothe online e-commerce system within a pre-determined period of time.

At operation 520, the computerized method 500 includes, for a seedlisting of the set of target listings, retrieving a seed imageassociated with the seed listing, the seed listing is categorized withina first item category. At operation 530, the computerized method 500includes generating a seed item feature vector for the seed image usinga convolutional neural network (CNN) trained with images of items, theseed image is an input to the CNN, the seed item feature vector includesan array of values representing features of the image. In someembodiments, the CNN uses the GoogLeNet model.

In the example embodiment, at operation 540, the computerized method 500also includes identifying a plurality of feature vectors associated withthe first item category, each feature vector of the plurality of featurevectors is associated with a listing categorized within the first itemcategory. At operation 550, the computerized method 500 further includescomparing the seed item feature vector to the plurality of featurevectors using a k-nearest neighbors (kNN) algorithm. At operation 560,the computerized method 500 also includes, based on the comparing,generating a set of nearest neighbor listings to the seed listing, eachnearest neighbor listing represents a listing for a item that isvisually similar to the seed item and that is categorized within thefirst item category. At operation 570, the computerized method 500further includes storing the set of nearest neighbor listings asassociated with the seed listing. At operation 580, the computerizedmethod 500 also includes selecting one or more nearest neighbor listingsfrom the set of nearest neighbors. At operation 590, the computerizedmethod 500 further includes presenting the one or more nearest neighborlistings as a recommendation to a user of an online e-commerce system.

In some embodiments, the computerized method 500 also includesgenerating a database entry including the listing identifier of the seeditem and listing identifiers of each listing in the set of nearestneighbor listings, and storing the database entry in an in-memorydatabase, wherein selecting one or more nearest neighbor listingsfurther includes selecting the one or more nearest neighbor listingsfrom the database entry. In some embodiments, the computerized methodfurther includes converting the seed image to a standard size prior togenerating the seed item feature vector, the standard size is used forimages used to generate the plurality of feature vectors. In someembodiments, the computerized method 500 also includes storing the seeditem feature vector along with the plurality of feature vectors for usein future similarity comparisons. In some embodiments, the computerizedmethod 500 further includes identifying a labeled set of imagesassociated with the first item category, each image in the labeled setof images includes a label based on a first category factor, the firstcategory factor identifies a factor applicable to items within the firstitem category, and training the CNN based on the labeled set of images.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A “hardware module” is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application specificintegrated circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software encompassed within a general-purpose processor orother programmable processor. It will be appreciated that the decisionto implement a hardware module mechanically, in dedicated andpermanently configured circuitry, or in temporarily configured circuitry(e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software mayaccordingly configure a particular processor or processors, for example,to constitute a particular hardware module at one instance of time andto constitute a different hardware module at a different instance oftime.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an application programinterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Software Architecture

FIG. 6 is a block diagram 600 illustrating an example softwarearchitecture 602, which may be used, in conjunction with varioushardware architectures herein described, to perform image-based analysisof listings (e.g., on the online e-commerce system 100). A imageanalysis engine 680, which is shown in a laywer of applications 620, maybe similar to the image analysis engine 150, but may be provided inwhole or in part at other layers shown in FIG. 6 . FIG. 6 is anon-limiting example of a software architecture 602, and it will beappreciated that many other architectures may be implemented tofacilitate the functionality described herein. The software architecture602 may execute on hardware such as a machine 700 of FIG. 7 thatincludes, among other things, processors 710, memory 730, andinput/output (I/O) components 750. A representative hardware layer 604is illustrated and can represent, for example, the machine 700 of FIG. 7. The representative hardware layer 604 includes a processing unit 606having associated executable instructions 608. The executableinstructions 608 represent the executable instructions of the softwarearchitecture 602, including implementation of the methods, modules, andso forth described herein. The hardware layer 604 also includesmemory/storage 610, which also includes the executable instructions 608.The hardware layer 604 may also comprise other hardware 612.

In the example architecture of FIG. 6 , the software architecture 602may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 602 mayinclude layers such as an operating system 614, libraries 616,frameworks or middleware 618, applications 620, and a presentation layer644. Operationally, the applications 620 and/or other components withinthe layers may invoke application programming interface (API) calls 624through the software stack and receive a response as messages 626. Thelayers illustrated are representative in nature and not all softwarearchitectures 602 have all layers. For example, some mobile or specialpurpose operating systems 614 may not provide the frameworks/middleware618, while others may provide such a layer. Other software architectures602 may include additional or different layers.

The operating system 614 may manage hardware resources and providecommon services. The operating system 614 may include, for example, akernel 628, services 630, and drivers 632. The kernel 628 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 628 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 630 may provideother common services for the other software layers. The drivers 632 maybe responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 632 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 616 may provide a common infrastructure that may be usedby the applications 620 and/or other components and/or layers. Thelibraries 616 typically provide functionality that allows other softwaremodules to perform tasks in an easier fashion than by interfacingdirectly with the underlying operating system 614 functionality (e.g.,kernel 628, services 630, and/or drivers 632). The libraries 616 mayinclude system libraries 634 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematic functions, and the like. In addition, thelibraries 616 may include API libraries 636 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D graphic content on a display), database libraries (e.g., SQLite thatmay provide various relational database functions), web libraries (e.g.,WebKit that may provide web browsing functionality), and the like. Thelibraries 616 may also include a wide variety of other libraries 638 toprovide many other APIs to the applications 620 and other softwarecomponents/modules.

The frameworks 618 (also sometimes referred to as middleware) provide ahigher-level common infrastructure that may be used by the applications620 and/or other software components/modules. For example, theframeworks/middleware 618 may provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks/middleware 618 may provide abroad spectrum of other APIs that may be utilized by the applications620 and/or other software components/modules, some of which may bespecific to a particular operating system 614 or platform.

The applications 620 include built-in applications 640 and/orthird-party applications 642. Examples of representative built-inapplications 640 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 642 may includean application developed using the Android™ or iOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system 614 suchas iOS™, Android™, Windows® Phone, or other mobile operating systems614. The third-party applications 642 may invoke the API calls 624provided by the mobile operating system, such as the operating system614, to facilitate functionality described herein.

The applications 620 may use built-in operating system functions (e.g.,kernel 628, services 630, and/or drivers 632), libraries 616, orframeworks/middleware 618 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 644. In these systems, the application/module“logic” can be separated from the aspects of the application/module thatinteract with a user.

Some software architectures 602 use virtual machines. In the example ofFIG. 6 , this is illustrated by a virtual machine 648. The virtualmachine 648 creates a software environment where applications/modulescan execute as if they were executing on a hardware machine (such as themachine 700 of FIG. 7 , for example). The virtual machine 648 is hostedby a host operating system (e.g., operating system 614) and typically,although not always, has a virtual machine monitor 646, which managesthe operation of the virtual machine 648 as well as the interface withthe host operating system (i.e., operating system 614). A softwarearchitecture executes within the virtual machine 648, such as anoperating system (OS) 650, libraries 652, frameworks 654, applications656, and/or a presentation layer 658. These layers of softwarearchitecture executing within the virtual machine 648 can be the same ascorresponding layers previously described or may be different.

FIG. 7 is a block diagram illustrating components of a machine 700,according to some example embodiments, configured to read instructions716 from a machine-readable medium 738 (e.g., a machine-readable storagemedium) and perform any one or more of the methodologies discussedherein. Specifically, FIG. 7 shows a diagrammatic representation of themachine 700 in the example form of a computer system, within whichinstructions 716 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 700 to performany one or more of the methodologies discussed herein may be executed.As such, the instructions 716 may be used to implement modules orcomponents described herein. The instructions 716 transform the general,non-programmed machine 700 into a particular machine programmed to carryout the described and illustrated functions in the manner described. Inalternative embodiments, the machine 700 operates as a standalone deviceor may be coupled (e.g., networked) to other machines. In a networkeddeployment, the machine 700 may operate in the capacity of a servermachine or a client machine in a server-client network environment, oras a peer machine in a peer-to-peer (or distributed) networkenvironment. The machine 700 may comprise, but not be limited to, aserver computer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a personal digital assistant(PDA), a cellular telephone, a smart phone, a mobile device, or anymachine capable of executing the instructions 716, sequentially orotherwise, that specify actions to be taken by the machine 700. Further,while only a single machine 700 is illustrated, the term “machine” shallalso be taken to include a collection of machines 700 that individuallyor jointly execute the instructions 716 to perform any one or more ofthe methodologies discussed herein.

The machine 700 may include processors 710, memory 730, and input/output(I/O) components 750, which may be configured to communicate with eachother such as via a bus 702. In an example embodiment, the processors710 (e.g., a central processing unit (CPU), a reduced instruction setcomputing (RISC) processor, a complex instruction set computing (CISC)processor, a GPU, a digital signal processor (DSP), an ASIC, aradio-frequency integrated circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, a processor 712and a processor 714 that may execute the instructions 716. The term“processor” is intended to include a multi-core processor 712 that maycomprise two or more independent processors 712, 714 (sometimes referredto as “cores”) that may execute the instructions 716 contemporaneously.Although FIG. 7 shows multiple processors 712, 714, the machine 700 mayinclude a single processor 712 with a single core, a single processor712 with multiple cores (e.g., a multi-core processor), multipleprocessors 712, 714 with a single core, multiple processors 712, 714with multiples cores, or any combination thereof.

The memory/storage 730 may include a memory, such as a main memory 732,a static memory 734, or other memory, and a storage unit 736, bothaccessible to the processors 710 such as via the bus 702. The storageunit 736 and memory 732, 734 store the instructions 716 embodying anyone or more of the methodologies or functions described herein. Theinstructions 716 may also reside, completely or partially, within thememory 732, 734, within the storage unit 736, within at least one of theprocessors 710 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine700. Accordingly, the memory 732, 734, the storage unit 736, and thememory of the processors 710 are examples of machine-readable media 738.

As used herein, “machine-readable medium” means a device able to storethe instructions 716 and data temporarily or permanently and mayinclude, but is not limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, optical media, magneticmedia, cache memory, other types of storage (e.g., erasable programmableread-only memory (EEPROM)), and/or any suitable combination thereof. Theterm “machine-readable medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,or associated caches and servers) able to store the instructions 716.The term “machine-readable medium” shall also be taken to include anymedium, or combination of multiple media, that is capable of storinginstructions (e.g., instructions 716) for execution by a machine (e.g.,machine 700), such that the instructions 716, when executed by one ormore processors of the machine 700 (e.g., processors 710), cause themachine 700 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

The input/output (I/O) components 750 may include a wide variety ofcomponents to receive input, provide output, produce output, transmitinformation, exchange information, capture measurements, and so on. Thespecific I/O components 750 that are included in a particular machine700 will depend on the type of machine. For example, portable machines700 such as mobile phones will likely include a touch input device orother such input mechanisms, while a headless server machine 700 willlikely not include such a touch input device. It will be appreciatedthat the I/O components 750 may include many other components that arenot shown in FIG. 7 . The I/O components 750 are grouped according tofunctionality merely for simplifying the following discussion and thegrouping is in no way limiting. In various example embodiments, the I/Ocomponents 750 may include output components 752 and input components754. The output components 752 may include visual components (e.g., adisplay such as a plasma display panel (PDP), a light emitting diode(LED) display, a liquid crystal display (LCD), a projector, or a cathoderay tube (CRT)), acoustic components (e.g., speakers), haptic components(e.g., a vibratory motor, resistance mechanisms), other signalgenerators, and so forth. The input components 754 may includealphanumeric input components (e.g., a keyboard, a touch screenconfigured to receive alphanumeric input, a photo-optical keyboard, orother alphanumeric input components), point based input components(e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, oranother pointing instrument), tactile input components (e.g., a physicalbutton, a touch screen that provides location and/or force of touches ortouch gestures, or other tactile input components), audio inputcomponents (e.g., a microphone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 750 may include communication components 764 operableto couple the machine 700 to a network 780 or devices 770 via a coupling782 and a coupling 772 respectively. For example, the communicationcomponents 764 may include a network interface component or othersuitable device to interface with the network 780. In further examples,the communication components 764 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, near field communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 770 may be another machine 700 or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Term Usage

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within the scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

1. A method comprising: retrieving a seed image of a seed item associated with a listing representing an item for sale, the listing categorized within an item category; generating a seed item feature vector for the seed image by providing the seed image as an input to a neural network model trained with images of items labeled with the item category, the seed item feature vector including an array of values representing features of the seed image; retrieving, from a database, one or more feature vectors associated with one or more respective listings categorized within the item category; generating a set of listings that are visually similar to the listing based on comparing the seed item feature vector to the one or more feature vectors; receiving a query for the listing; and responsive to the query, selecting one or more listings from the set of listings and communicating the one or more listings as a recommendation.
 2. The method of claim 1, further comprising selecting the seed image of the seed item to retrieve based on a recentness of the seed image satisfying a recency threshold, a processing status of the listing, or both.
 3. The method of claim 1, further comprising storing, at the database, the seed item feature vector for the seed image.
 4. The method of claim 1, wherein the database comprises a plurality of feature vectors associated with respective listings of items for sale.
 5. The method of claim 1, wherein the listing comprises the seed image, an indication of the item category, and a listing identifier associated with the listing.
 6. The method of claim 1, further comprising storing, based on generating the set of listings, a plurality of database entries representative of the set of listings, wherein communicating the one or more listings is based on retrieving one or more database entries corresponding to the one or more listings.
 7. The method of claim 1, further comprising generating, a duration after generating the set of listings and based on at least one feature vector being added to the database, a second set of listings that are visually similar to the listing.
 8. The method of claim 1, wherein the neural network model is a convolutional neural network (CNN) model.
 9. The method of claim 1, further comprising converting the seed image from a first size to a second size prior to generating the seed item feature vector.
 10. A system comprising: at least one processor; and a computer-readable storage medium storing instructions that are executable by the at least one processor to perform operations comprising: retrieving a seed image of a seed item associated with a listing representing an item for sale, the listing categorized within an item category; generating a seed item feature vector for the seed image by providing the seed image as an input to a neural network model trained with images of items labeled with the item category, the seed item feature vector including an array of values representing features of the seed image; retrieving, from a database, one or more feature vectors associated with one or more respective listings categorized within the item category; generating a set of listings that are visually similar to the listing based on comparing the seed item feature vector to the one or more feature vectors; receiving a query for the listing; and responsive to the query, selecting one or more listings from the set of listings and communicating the one or more listings as a recommendation.
 11. The system of claim 10, the operations further comprising selecting the seed image of the seed item to retrieve based on a recentness of the seed image satisfying a recency threshold, a processing status of the listing, or both.
 12. The system of claim 10, the operations further comprising storing, at the database, the seed item feature vector for the seed image.
 13. The system of claim 10, wherein the database comprises a plurality of feature vectors associated with respective listings of items for sale.
 14. The system of claim 10, wherein the listing comprises the seed image, an indication of the item category, and a listing identifier associated with the listing.
 15. The system of claim 10, the operations further comprising storing, based on generating the set of listings, a plurality of database entries representative of the set of listings, wherein communicating the one or more listings is based on retrieving one or more database entries corresponding to the one or more listings.
 16. The system of claim 10, the operations further comprising generating, a duration after generating the set of listings and based on at least one feature vector being added to the database, a second set of listings that are visually similar to the listing.
 17. The system of claim 10, wherein the neural network model is a convolutional neural network (CNN) model.
 18. The system of claim 10, the operations further comprising converting the seed image from a first size to a second size prior to generating the seed item feature vector.
 19. One or more non-transitory computer-readable storage media storing instructions that, responsive to execution by at least one processor, cause the at least one processor to perform operations comprising: retrieving a seed image of a seed item associated with a listing representing an item for sale, the listing categorized within an item category; generating a seed item feature vector for the seed image by providing the seed image as an input to a neural network model trained with images of items labeled with the item category, the seed item feature vector including an array of values representing features of the seed image; retrieving, from a database, one or more feature vectors associated with one or more respective listings categorized within the item category; generating a set of listings that are visually similar to the listing based on comparing the seed item feature vector to the one or more feature vectors; receiving a query for the listing; and responsive to the query, selecting one or more listings from the set of listings and communicating the one or more listings as a recommendation.
 20. The one or more non-transitory computer-readable storage media of claim 19, the operations further comprising storing, based on generating the set of listings, a plurality of database entries representative of the set of listings, wherein communicating the one or more listings is based on retrieving one or more database entries corresponding to the one or more listings. 